On the Evaluation of Machine Translation n-best Lists
Jacob Bremerman, Huda Khayrallah, Douglas W. Oard, Matt Post · 2020
The standard machine translation evaluation framework measures the single-best output of machine translation systems.There are, however, many situations where n-best lists are needed, yet there is no established way of evaluating them.This paper establishes a framework for addressing n-best evaluation by outlining three different questions one could consider when determining how one would define a 'good' n-best list and proposing evaluation measures for each question.The first and principal contribution is an evaluation measure that characterizes the translation quality of an entire n-best list by asking whether many of the valid translations are placed near the top of the list.The second is a measure that uses gold translations with preference annotations to ask to what degree systems can produce ranked lists in preference order.The third is a measure that rewards partial matches, evaluating the closeness of the many items in an n-best list to a set of many valid references.These three perspectives make clear that having access to many references can be useful when n-best evaluation is the goal.